PROJECT SUMMARY ? PROJECT 3 Comprehensive early treatment of individuals experiencing schizophrenia has the potential to alter the course of illness and improve long-term outcomes. Psychotropic medications are a critical component of early treatment strategies. Because early experiences with medications may have an enduring effect on attitudes toward medications and adherence, first-episode psychosis (FEP) is a critical time to optimize prescribing. Yet evidence suggests that prescribing for this population is suboptimal, with many patients receiving higher than recommended dosages of antipsychotic medications and, for unclear indications, additional psychotropic medications. A contributing factor to these difficulties is the lack of accurate information about the effects of medications on symptoms, their side effects, as well as their behavioral, cognitive, and emotional correlates. At medication management appointments, prescribers typically rely on patients' recollection of how they were doing over periods of weeks. Such retrospective assessments are problematic as they are vulnerable to the influence of memory difficulties and cognitive biases. To address these issues, this project will use a smartphone technology to improve medication prescribing for individuals with FEP. We will collect real-time symptom and functioning data via smartphones to provide prescribers and other clinical team members with clinically relevant and time-sensitive information that will inform and promote shared decision making (SDM) and personalized interventions. The result will be a time-sensitive, data-driven, collaborative process to optimize medication regimens in order to maximize benefits, minimize harms, and promote adherence. We will conduct a pilot study in collaboration with OnTrackNY, an innovative coordinated specialty care (CSC) program for individuals aged 16-30 who are experiencing FEP. Participants will be randomized to receive the mHealth intervention or treatment as usual. We will evaluate the feasibility of the intervention and its effects on treatment satisfaction, functioning, quality of life, symptoms and side effects. This pilot study will provide feasibility data for a full-scale effectiveness study. It will generate an mHealth intervention that explicitly incorporates patient input, is scalable, and leads to improved, data-driven psychopharmacologic treatment.